Increasing water consumption has increased using of synthetic nutritional methods for enriching g... more Increasing water consumption has increased using of synthetic nutritional methods for enriching groundwater resources. Artificial feeding is a method that can save excess water for using in low level water time in underground. The purpose of this study is to evaluate the performance of the flood dispersal and artificial feeding system in the Red Garden of Shahr-e-Daghshan and improving, saving quality of the groundwater table in the area. In order to investigate the performance of these plans, an area of 1570 km2 was considered in the Southern of Shah-Reza. The statistics data from 5 years before the design of the plans (1986-2002) related to flood control fluctuations in 20 observation wells and many indicator Qanat were surveyed in this area. The annual fluctuations in the level of the station show a rise in the level of the station after the depletion of the plan. Dewatering of the first and second turns, with an increase of more than one meter above groundwater level, has had the highest impact on the level of groundwater table in the region. Reduced permeability at sediment levels, wasted flood through evaporation and wasteful exploitation of groundwater resources, cause to loss of the impact on the increase in the level and quality of groundwater in the area, especially in the dry, drought season and recent high droughts.
River flow forecasting is of immense importance for reliable planning, designing, and management ... more River flow forecasting is of immense importance for reliable planning, designing, and management of water resources projects. This study investigated the performance of wavelet neural network, support vector machine, artificial neural network, and Multiple Models Driven by Artificial Neural Networks (MMANN) in predicting flow time series of the Kashkan River in Lorestan, Iran. Daily flow time series was created from the records of Kashkan hydrometric and rain gauge stations for a 10-year period from 2006 to 2016. To determine the best input-output mapping, estimations were repeated with different combinations of inputs derived from previous daily river flow data. Performance of the models was evaluated in terms of correlation coefficient, root mean square error, and mean absolute error. Performance comparisons showed that the MMANN model with a correlation coefficient of 0.960, root mean square error of 0.021, and mean absolute error of 0.001 generates the best daily flow estimates for the studied river.
Increasing water consumption has increased using of synthetic nutritional methods for enriching g... more Increasing water consumption has increased using of synthetic nutritional methods for enriching groundwater resources. Artificial feeding is a method that can save excess water for using in low level water time in underground. The purpose of this study is to evaluate the performance of the flood dispersal and artificial feeding system in the Red Garden of Shahr-e-Daghshan and improving, saving quality of the groundwater table in the area. In order to investigate the performance of these plans, an area of 1570 km2 was considered in the Southern of Shah-Reza. The statistics data from 5 years before the design of the plans (1986-2002) related to flood control fluctuations in 20 observation wells and many indicator Qanat were surveyed in this area. The annual fluctuations in the level of the station show a rise in the level of the station after the depletion of the plan. Dewatering of the first and second turns, with an increase of more than one meter above groundwater level, has had the highest impact on the level of groundwater table in the region. Reduced permeability at sediment levels, wasted flood through evaporation and wasteful exploitation of groundwater resources, cause to loss of the impact on the increase in the level and quality of groundwater in the area, especially in the dry, drought season and recent high droughts.
River flow forecasting is of immense importance for reliable planning, designing, and management ... more River flow forecasting is of immense importance for reliable planning, designing, and management of water resources projects. This study investigated the performance of wavelet neural network, support vector machine, artificial neural network, and Multiple Models Driven by Artificial Neural Networks (MMANN) in predicting flow time series of the Kashkan River in Lorestan, Iran. Daily flow time series was created from the records of Kashkan hydrometric and rain gauge stations for a 10-year period from 2006 to 2016. To determine the best input-output mapping, estimations were repeated with different combinations of inputs derived from previous daily river flow data. Performance of the models was evaluated in terms of correlation coefficient, root mean square error, and mean absolute error. Performance comparisons showed that the MMANN model with a correlation coefficient of 0.960, root mean square error of 0.021, and mean absolute error of 0.001 generates the best daily flow estimates for the studied river.
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